P. Barth. A Davis-Putnam Enumeration Algorithm for Linear Pseudo-Boolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995. Home/Search Document Not in Database Summary Related Articles Check |
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On Using Satisfiability-Based Pruning Techniques in.. - Vasco Manquinho Joao (2000) (4 citations) (Correct)
....[5] and the process of repeatedly applying this rule is called boolean constraint propagation [14] We should note that throughout the remainder of this paper some familiarity with backtrack search SAT algorithms is assumed. The interested reader is referred to the bibliography (see for example [1, 14] for additional references) Covering problems are often solved by branch and bound algorithms [3, 8, 15] In these cases, each node of the search tree corresponds to a selected unassigned variable and the two branches out of the node represent the assignment of 1 and 0 to that variable. These ....
....the ones based on boolean satisfiability algorithms, have different pruning strategies which are not commonly used in branch and bound algorithms for solving BCP. In section 3.2 an algorithm which combines features from both approaches is described. 3.1. SAT Based Linear Search Algorithm In [1] P. Barth describes how to solve pseudo boolean optimization (i.e. a generalization of BCP) using a propositional satisfiability (SAT) algorithm. However, the algorithm described in [1] is based on the Davis Putnam [5] procedure, which has been shown to be particularly inefficient for a large ....
[Article contains additional citation context not shown here]
P. Barth. A Davis-Putnam Enumeration Algorithm for Linear Pseudo-Boolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995.
Lower Bounding Techniques for SAT-based Boolean Optimization - Marques-Silva (Correct)
....Introduction The generic Boolean Optimization problem as well as several of its restrictions are wellknown computationally hard problems, widely used as modeling tools in Computer Science and Engineering. These problems have been the subject of extensive research work in the past (see for example [1]) In this paper we address the Binate Covering Problem (BCP) one of the restrictions of Boolean Optimization. BCP can be formulated as the problem of nding a satisfying assignment for a given Conjunctive Normal Form (CNF) formula subject to minimizing a given cost function. As with generic ....
....the process of repeatedly applying this rule is called boolean constraint propagation [13, 16] It should be noted that throughout the remainder of this paper some familiarity with backtrack search SAT algorithms is assumed. The interested reader is referred to the bibliography (see for example [1, 13] for additional references) Covering problems are often solved by branch and bound algorithms [4, 7, 14] In these cases, each node of the search tree corresponds to a selected unassigned variable and the two branches out of the node represent the assignment of 1 and 0 to that variable. These ....
[Article contains additional citation context not shown here]
P. Barth. A Davis-Putnam Enumeration Algorithm for Linear Pseudo-Boolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995.
Search Pruning Conditions for Boolean Optimization - Manquinho, Marques-Silva (2000) (Correct)
....The generic Boolean Optimization problem as well as several of its restrictions are well known computationally hard problems, widely used as modeling tools in Computer Science and Engineering. These problems have been the subject of extensive research work in the past (see for example [1]) In this paper we address the Binate Covering Problem (BCP) one of the restrictions of Boolean Optimization. BCP can be formulated as the problem of finding a satisfying assignment for a given Conjunctive Normal Form (CNF) formula subject to minimizing a given cost function. As with generic ....
....the process of repeatedly applying this rule is called boolean constraint propagation [14, 16] It should be noted that throughout the remainder of this paper some familiarity with backtrack search SAT algorithms is assumed. The interested reader is referred to the bibliography (see for example [1, 14] for additional references) Covering problems are often solved by branch and bound algorithms [5, 8, 15] In these cases, each node of the search tree corresponds to a selected unassigned variable and the two branches out of the node represent the assignment of 1 and 0 to that variable. These ....
[Article contains additional citation context not shown here]
P. Barth. A Davis-Putnam Enumeration Algorithm for Linear PseudoBoolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995.
Conditions for Non-Chronological Backtracking in Boolean.. - Manquinho, Marques-Silva (2000) (Correct)
....The generic Boolean Optimization problem as well as several of its restrictions are well known computationally hard problems, widely used as modeling tools in Computer Science and Engineering. These problems have been the subject of extensive research work in the past (see for example [1]) In this paper we address the Binate Covering Problem (BCP) one of the restrictions of Boolean Optimization. BCP can be formulated as the problem of finding a satisfying assignment for a given Conjunctive Normal Form (CNF) formula subject to minimizing a given cost function. As with generic ....
....the process of repeatedly applying this rule is called boolean constraint propagation [13, 15] It should be noted that throughout the remainder of this paper some familiarity with backtrack search SAT algorithms is assumed. The interested reader is referred to the bibliography (see for example [1, 13] for additional references) Covering problems are often solved by branch and bound algorithms [5, 8, 14] In these cases, each node of the search tree corresponds to a selected unassigned variable and the two branches out of the node represent the assignment of 1 and 0 to that variable. These ....
[Article contains additional citation context not shown here]
P. Barth. A Davis-Putnam Enumeration Algorithm for Linear PseudoBoolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995.
On Using Satisfiability-Based Pruning Techniques in.. - Vasco Manquinho Joao (2000) (4 citations) (Correct)
....[5] and the process of repeatedly applying this rule is called boolean constraint propagation [14] We should note that throughout the remainder of this paper some familiarity with backtrack search SAT algorithms is assumed. The interested reader is referred to the bibliography (see for example [1, 14] for additional references) Covering problems are often solved by branch and bound algorithms [3, 8, 15] In these cases, each node of the search tree corresponds to a selected unassigned variable and the two branches out of the node represent the assignment of 1 and 0 to that variable. These ....
....the ones based on boolean satisfiability algorithms, have different pruning strategies which are not commonly used in branch and bound algorithms for solving BCP. In section 3.2 an algorithm which combines features from both approaches is described. 3.1. SAT Based Linear Search Algorithm In [1] P. Barth describes how to solve pseudo boolean optimization (i.e. a generalization of BCP) using a propositional satisfiability (SAT) algorithm. However, the algorithm described in [1] is based on the Davis Putnam [5] procedure, which has been shown to be particularly inefficient for a large ....
[Article contains additional citation context not shown here]
P. Barth. A Davis-Putnam Enumeration Algorithm for Linear Pseudo-Boolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995.
On Solving Boolean Optimization with Satisfiability-Based .. - Manquinho, Marques-Silva (2000) (1 citation) (Correct)
....The generic Boolean Optimization problem as well as several of its restrictions are well known computationally hard problems, widely used as modeling tools in Computer Science and Engineering. These problems have been the subject of extensive research work in the past (see for example [1]) In this paper we address the Binate Covering Problem (BCP) one of the restrictions of Boolean Optimization. BCP can be formulated as the problem of finding a satisfying for a given Conjunctive Normal Form (CNF) formula subject to minimizing a given cost function. As with generic Boolean ....
....[7] and the process of repeatedly applying this rule is called boolean constraint propagation [17] We should note that throughout the remainder of this paper some familiarity with backtrack search SAT algorithms is assumed. The interested reader is referred to the bibliography (see for example [1, 17] for additional references) Covering problems are often solved by branch and bound algorithms [4, 10, 18] In these cases, each node of the search tree corresponds to a selected unassigned variable and the two branches out of the node represent the assignment of 1 and 0 to that variable. These ....
[Article contains additional citation context not shown here]
P. Barth. A Davis-Putnam Enumeration Algorithm for Linear Pseudo-Boolean Optimization. Technical Report MPI-I-95-2-003, Max Plank Institute for Computer Science, 1995.
Algorithms for Solving Boolean Satisfiability in.. - Silva, Silveira.. (1999) (9 citations) (Correct)
.... of binate covering problems (BCP) 6, 9, 10, 11, 12] in particular for those in which the constraints are hard to satisfy, e.g. in computing minimum size test patterns [10] SAT also plays a key role in other domains, including for example Artificial Intelligence [3, 19] and Operations Research [2]. Recent years have seen dramatic improvements in SAT algorithms, which have been thoroughly validated in different application areas [3, 15, 19] With respect to applications of SAT in EDA, in most cases the original problem formulation starts from a circuit description, for which a given ....
P. Barth, "A Davis-Putnam Enumeration Algorithm for Linear pseudo-Boolean Optimization," Technical Report MPII -95-2-003, Max Planck Institute for Computer Science, 1995.
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